In thesis

Lindgren, Tony

Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.

2006 (English)Doctoral thesis, comprehensive summary (Other academic)

Abstract [en]

When applying an unordered set of classification rules to classify an example, there may be several applicable rules with conflicting conclusions regarding the most probable class to which the example belongs. This problem of having rules assigning different classes to the same example must be addressed, if a classification is to be made. The standard methods of resolving such conflicts include using the most frequent class in the examples covered by the conflicting rules and using naive Bayes to calculate the most probable class.

This thesis presents four papers, in each of which the problem of conflicting rules is addressed. In the first paper, a method that bridges the gap between Bayes rule and naive Bayes is presented. The second paper presents a data driven method for resolving rule conflicts, and the third paper explores this data driven approach further. In the last paper, a method for resolving rule conflicts in domains where the examples have numerical features is presented.

For all new methods of solving rule conflicts, it is shown that the novel methods outperform the standard methods. A correlation between the novel methods performance and their computational cost is found: usually the more costly methods obtain a higher accuracy than the less costly methods.